scholarly journals Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma

2020 ◽  
Vol 20 (2) ◽  
pp. 1573-1584
Author(s):  
Hao Cui ◽  
Lei Xu ◽  
Zhi Li ◽  
Ke‑Zuo Hou ◽  
Xiao‑Fang Che ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

2020 ◽  
Vol 52 (8) ◽  
pp. 853-863
Author(s):  
Wenxin Zhai ◽  
Haijiao Lu ◽  
Shenghua Dong ◽  
Jing Fang ◽  
Zhuang Yu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein–protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

scholarly journals PPARα gene is a diagnostic and prognostic biomarker in clear cell renal cell carcinoma by integrated bioinformatics analysis

2019 ◽  
Vol 10 (10) ◽  
pp. 2319-2331
Author(s):  
Yongwen Luo ◽  
Liang Chen ◽  
Gang Wang ◽  
Guofeng Qian ◽  
Xuefeng Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Prognostic Biomarker ◽  
Cell Renal Cell Carcinoma

scholarly journals LRRK2 is a Candidate Prognostic Biomarker for Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Chunxiu Yang ◽  
Jingjing Pang ◽  
Jian Xu ◽  
He Pan ◽  
Yueying Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Target Therapy ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Gene Target

Abstract Background: Clear cell renal cell carcinoma (ccRCC), derived from renal tubular epithelial cells, is the most common malignant tumor of the kidney. The study of key genes related to the pathogenesis of ccRCC has become important for gene target therapy. Methods: Bioinformatics analysis of The Cancer Genome Atlas (TCGA) and the NCBI Gene Expression Omnibus (GEO) database were performed to examine the expression pattern and prognostic significance of leucine-rich repeat kinase 2 (LRRK2) expression and its relationship to clinical parameters. Immunohistochemistry and Western blot were performed to verify LRRK2 expression.Results: Bioinformatics analysis showed that LRRK2 expression was up-regulated in ccRCC, which was confirmed in ccRCC tissue immunohistochemically and by protein analysis. The level of expression was related to gender, pathological grade, stage and metastatic status of ccRCC patients. Meanwhile, Kaplan-Meier analysis showed that high expression of LRRK2 correlates to a better prognosis; protein-protein interaction network analysis showed that LRRK2 interacts with HIF1A and EGFR.Conclusion: We found that LRRK2 may play an important role in the tumorigenesis and progression of ccRCC. Our findings provided a potential predictor and therapeutic target in ccRCC.

scholarly journals Identification of 9 key genes and small molecule drugs in clear cell renal cell carcinoma

Aging ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 6029-6052 ◽  
Author(s):  
Yongwen Luo ◽  
Dexin Shen ◽  
Liang Chen ◽  
Gang Wang ◽  
Xuefeng Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Small Molecule ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Small Molecule Drugs ◽  
Key Genes

scholarly journals Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Dengyong Xu ◽  
Yuzi Xu ◽  
Yiming Lv ◽  
Fei Wu ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Early Diagnosis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Clinical Stage ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a major histological subtype of renal cell carcinoma and can be clinically divided into four stages according to the TNM criteria. Identifying clinical stage-related genes is beneficial for improving the early diagnosis and prognosis of ccRCC. By using bioinformatics analysis, we aim to identify clinical stage-relevant genes that are significantly associated with the development of ccRCC. First, we analyzed the gene expression microarray data sets: GSE53757 and GSE73731. We divided these data into five groups by staging information—normal tissue and ccRCC stages I, II, III, and IV—and eventually identified 500 differentially expressed genes (DEGs). To obtain precise stage-relevant genes, we subsequently applied weighted gene coexpression network analysis (WGCNA) to the GSE73731 dataset and KIRC data from The Cancer Genome Atlas (TCGA). Two modules from each dataset were identified to be related to the tumor TNM stage. Several genes with high inner connection inside the modules were considered hub genes. The intersection results between hub genes of key modules and 500 DEGs revealed UBE2C, BUB1B, RRM2, and TPX2 as highly associated with the stage of ccRCC. In addition, the candidate genes were validated at both the RNA expression level and the protein level. Survival analysis also showed that 4 genes were significantly correlated with overall survival. In conclusion, our study affords a deeper understanding of the molecular mechanisms associated with the development of ccRCC and provides potential biomarkers for early diagnosis and individualized treatment for patients at different stages of ccRCC.

scholarly journals Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 452-461
Author(s):  
Fangyuan Zhang ◽  
Pengjie Wu ◽  
Yalong Wang ◽  
Mengxian Zhang ◽  
Xiaodan Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma
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